Here is a spinning globe with population density drawn. This was code written in 20 min, and took about 100 min to render the images on my laptop. Basemap is straight forward to use... once you know what you're doing. However, the maps don't seem quite as mature as those in IDL (to be fair, IDL has been a commercial mapping product for many years). If you'd like to use this as an example for learning Basemap/Python, the code is on GitHub! I am also working to make the same map using IDL as a teaching tool.

If you've ever watched a professional football game (and this is probably true for most professional sports) then you have seen these little portraits of players that appear at the bottom of the screen. On some TV networks they are actually short video clips where the players announce their alma mater, on some networks they are animations where the players each raise their heads and occasionally blink (these creep me out), and for other networks these head-shots are just still photos. Some players smile in their photos, some do not.

Key & Peele have a recurring bit about this player introduction phenomenon.

While watching a Seahawks game this past year, my mother in law posed an amusing question: Do players who smile in their photos play better football?
The question is simple and whimsical, in other words perfect. I don't know anything about how often these photos are taken, what the player's mindset is when they're shot, or if there is any prior expectation about attitude/persona and player record. I set out to find some answers...

For this study I am only focusing on Quarter Backs (QBs) in American professional footbal (NFL), though it would be easy to extend to all positions if anyone can help me get the data! Right away I know I'll need a few ingredients: photos of each player, some classification of their smiles, and some real stats on their records in the NFL.